Predictive Analytics

Startup success prediction and VC portfolio simulation using CrunchBase data ArXiv ID: 2309.15552 “View on arXiv” Authors: Unknown Abstract Predicting startup success presents a formidable challenge due to the inherently volatile landscape of the entrepreneurial ecosystem. The advent of extensive databases like Crunchbase jointly with available open data enables the application of machine learning and artificial intelligence for more accurate predictive analytics. This paper focuses on startups at their Series B and Series C investment stages, aiming to predict key success milestones such as achieving an Initial Public Offering (IPO), attaining unicorn status, or executing a successful Merger and Acquisition (M&A). We introduce novel deep learning model for predicting startup success, integrating a variety of factors such as funding metrics, founder features, industry category. A distinctive feature of our research is the use of a comprehensive backtesting algorithm designed to simulate the venture capital investment process. This simulation allows for a robust evaluation of our model’s performance against historical data, providing actionable insights into its practical utility in real-world investment contexts. Evaluating our model on Crunchbase’s, we achieved a 14 times capital growth and successfully identified on B round high-potential startups including Revolut, DigitalOcean, Klarna, Github and others. Our empirical findings illuminate the importance of incorporating diverse feature sets in enhancing the model’s predictive accuracy. In summary, our work demonstrates the considerable promise of deep learning models and alternative unstructured data in predicting startup success and sets the stage for future advancements in this research area. ...

VolTS: A Volatility-based Trading System to forecast Stock Markets Trend using Statistics and Machine Learning ArXiv ID: 2307.13422 “View on arXiv” Authors: Unknown Abstract Volatility-based trading strategies have attracted a lot of attention in financial markets due to their ability to capture opportunities for profit from market dynamics. In this article, we propose a new volatility-based trading strategy that combines statistical analysis with machine learning techniques to forecast stock markets trend. The method consists of several steps including, data exploration, correlation and autocorrelation analysis, technical indicator use, application of hypothesis tests and statistical models, and use of variable selection algorithms. In particular, we use the k-means++ clustering algorithm to group the mean volatility of the nine largest stocks in the NYSE and NasdaqGS markets. The resulting clusters are the basis for identifying relationships between stocks based on their volatility behaviour. Next, we use the Granger Causality Test on the clustered dataset with mid-volatility to determine the predictive power of a stock over another stock. By identifying stocks with strong predictive relationships, we establish a trading strategy in which the stock acting as a reliable predictor becomes a trend indicator to determine the buy, sell, and hold of target stock trades. Through extensive backtesting and performance evaluation, we find the reliability and robustness of our volatility-based trading strategy. The results suggest that our approach effectively captures profitable trading opportunities by leveraging the predictive power of volatility clusters, and Granger causality relationships between stocks. The proposed strategy offers valuable insights and practical implications to investors and market participants who seek to improve their trading decisions and capitalize on market trends. It provides valuable insights and practical implications for market participants looking to. ...

Trends and Applications of Machine Learning in QuantitativeFinance ArXiv ID: ssrn-3397005 “View on arXiv” Authors: Unknown Abstract Recent advances in machine learning are finding commercial applications across many industries, not least the finance industry. This paper focuses on applicatio Keywords: machine learning, algorithmic trading, predictive analytics, quantitative finance, Multi-Asset Complexity vs Empirical Score Math Complexity: 4.5/10 Empirical Rigor: 3.0/10 Quadrant: Philosophers Why: The paper is a broad literature review of ML applications in finance, focusing on conceptual categorization rather than novel mathematical derivations or empirical backtesting. It outlines common algorithms and use cases but lacks implementation details, statistical metrics, or specific experimental results. flowchart TD G["Research Goal: Evaluate ML in Quant Finance"] --> D["Data Sources"] D --> M["Key Methodology"] D --> C["Computational Processes"] M --> F["Key Findings/Outcomes"] C --> F subgraph D ["Data/Inputs"] D1["Multi-Asset Market Data"] D2["Historical Price & Volatility"] end subgraph M ["Methodology Steps"] M1["Algorithmic Trading Strategies"] M2["Predictive Analytics"] end subgraph C ["Computational Processes"] C1["Deep Learning Models"] C2["Reinforcement Learning"] end subgraph F ["Outcomes"] F1["Enhanced Portfolio Optimization"] F2["Improved Risk Management"] F3["Commercial Applications in Finance"] end

Advances in Financial Machine Learning: Lecture 1/10 (seminar slides) ArXiv ID: ssrn-3270329 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: machine learning, algorithmic trading, predictive analytics, data science, fintech, Multi-Asset / Quantitative Strategies Complexity vs Empirical Score Math Complexity: 3.5/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The excerpt presents a high-level critique of econometric methods compared to machine learning, but it focuses on theoretical arguments and conceptual pitfalls rather than advancing novel mathematical techniques or presenting concrete backtesting results. flowchart TD A["Research Goal: Apply ML to Financial Markets"] --> B["Methodology: Identify Financial Signals & Features"] B --> C["Data Inputs: High-Frequency Trading & Market Data"] C --> D["Computation: Training Algorithms & Model Validation"] D --> E["Outcomes: Predictive Analytics for Multi-Asset Strategies"]

Advances in Financial Machine Learning: Lecture 8/10 (seminar slides) ArXiv ID: ssrn-3270269 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning (ML), Predictive Analytics, Algorithmic Trading, Big Data, Equities Complexity vs Empirical Score Math Complexity: 8.0/10 Empirical Rigor: 3.0/10 Quadrant: Lab Rats Why: The excerpt features advanced statistical methods and formal derivations for detecting structural breaks and entropy estimation, but it lacks implementation details, backtests, or code, focusing instead on theoretical presentations suitable for academic exploration. flowchart TD Q["Research Goal: Can ML beat markets?"] D["Input: Big Data Equities"] P["Computational Process: Algorithmic Trading Models"] F["Outcome: Predictive Analytics"] E["Key Finding: Risk/Overfitting Constraints"] Q --> D D --> P P --> F F --> E

Advances in Financial Machine Learning: Lecture 3/10 (seminar slides) ArXiv ID: ssrn-3257419 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning, Artificial Intelligence, Algorithmic Trading, Predictive Analytics, Data Science, Equity Complexity vs Empirical Score Math Complexity: 6.0/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The paper introduces advanced financial data structures and labeling techniques like Fractionally Differentiated Features, Triple Barrier Method, and Meta-Labeling, involving statistical estimation and optimization, yet the provided excerpt is conceptual lecture slides without executable code, backtests, or specific datasets, limiting its immediate empirical implementation. flowchart TD A["Research Goal:<br>Predictive Analytics for Equity Markets"] --> B["Methodology: ML Algorithms"] A --> C["Data: Financial Time Series"] B --> D["Computational Process:<br>Feature Engineering & Backtesting"] C --> D D --> E["Outcome: Algorithmic Trading Signals"] D --> F["Outcome: Risk Assessment Models"] E --> G["Key Finding:<br>ML enhances trading efficiency"]

Advances in Financial Machine Learning: Lecture 5/10 (seminar slides) ArXiv ID: ssrn-3257497 “View on arXiv” Authors: Unknown Abstract Machine learning (ML) is changing virtually every aspect of our lives. Today ML algorithms accomplish tasks that until recently only expert humans could perform Keywords: Machine Learning (ML), Algorithmic Trading, Data Science, Predictive Analytics, Multi-Asset Complexity vs Empirical Score Math Complexity: 6.5/10 Empirical Rigor: 4.0/10 Quadrant: Lab Rats Why: The material features advanced statistical derivations, hypothesis testing, and combinatorial math for backtesting methods like CPCV, warranting a high math score. However, it lacks concrete code, dataset specifics, or reported backtest results, focusing instead on methodological warnings and theoretical frameworks, resulting in moderate empirical rigor. flowchart TD A["Research Goal: Assess ML Efficacy in Multi-Asset Algorithmic Trading"] --> B["Data Acquisition & Cleaning"] B --> C["Feature Engineering & Time-Series Splitting"] C --> D["Computational Process: Ensemble ML Models"] D --> E["Key Finding 1: ML Outperforms Traditional Econometrics"] D --> F["Key Finding 2: Meta-Labeling Improves Risk Management"] E --> G["Outcome: Enhanced Predictive Analytics for Financial Markets"] F --> G

The 10 Reasons Most Machine Learning Funds Fail ArXiv ID: ssrn-3104816 “View on arXiv” Authors: Unknown Abstract The rate of failure in quantitative finance is high, and particularly so in financial machine learning. The few managers who succeed amass a large amount of ass Keywords: Financial Machine Learning, Quantitative Finance, Asset Management, Predictive Analytics, Trading Strategy, Quantitative Finance / Equities Complexity vs Empirical Score Math Complexity: 2.0/10 Empirical Rigor: 1.5/10 Quadrant: Philosophers Why: The paper focuses on high-level methodological pitfalls and organizational paradigms in financial machine learning, with minimal advanced mathematical formalism. It lacks empirical backtests, statistical code, or implementation-heavy data analysis, making it more of a conceptual framework than a backtest-ready study. flowchart TD Q["Research Question:<br>Why do ML funds fail?"] --> D["Data: Financial ML<br>papers & strategies"] D --> M["Methodology: Cross-sectional<br>analysis of failures"] M --> C["Computational Process:<br>Identify recurring pitfalls"] C --> F["Findings: 10 systemic reasons<br>e.g., overfitting, data snooping"] F --> O["Outcome: Risk management<br>framework for ML funds"]

Fraud Detection and Expected Returns ArXiv ID: ssrn-1998387 “View on arXiv” Authors: Unknown Abstract An accounting-based model has strong out-of-sample power not only to detect fraud, but also to predict cross-sectional returns. Firms with a higher probabilit Keywords: Accounting-Based Models, Fraud Detection, Cross-Sectional Returns, Predictive Analytics, Financial Statement Analysis, Equity Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 7.0/10 Quadrant: Street Traders Why: The paper uses an accounting-based predictive model (high empirical data focus) with statistical validation and out-of-sample testing, but the mathematics described are primarily regression-based and do not involve advanced calculus or complex theoretical derivations. flowchart TD A["Research Goal: Does an accounting-based model<br>predict fraud AND future returns?"] --> B["Methodology: Predictive Analytics<br>Logistic Regression & Cross-Validation"] B --> C["Data Inputs:<br>Financial Statements & Stock Returns"] C --> D["Computational Process:<br>Estimate Prob(Fraud) using Accounting Ratios"] D --> E{"Key Findings"} E --> F["Strong Out-of-Sample Fraud Detection"] E --> G["Predict Cross-Sectional Returns"]